Just like human personal assistants, digital assistant systems can perform requested tasks and provide requested advice, information, or services. A digital assistant system's ability to fulfill a user's request is dependent on the digital assistant system's correct comprehension of the request or instructions. Recent advances in natural language processing have enabled users to interact with digital assistant systems using natural language, in spoken or textual forms. Such digital assistant systems can interpret the user's input to deduce the user's intent, translate the deduced intent into actionable tasks and parameters, execute operations or deploy services to perform the tasks, and produce output that is intelligible to the user. The ability of a digital assistant system to produce satisfactory responses to user requests depends on the natural language processing, knowledge base, and artificial intelligence available to the digital assistant system.
Also, users are increasingly using mobile devices to post status updates, messages, or blog posts to online services such as social networks, blogs, and the like. Traditionally, however, speech-to-text systems and/or digital assistants have been confined to information retrieval (e.g., web search), transcribing voice inputs for email or text messages, and the like, and have not been able to handle the special types of text that are sometimes used in postings to social networks, such as FACEBOOK or TWITTER. For example, a user may wish to input via a digital assistant special types of text, such as online handles or usernames (e.g., a TWITTER username), tags (e.g., a TWITTER hashtag), etc., that are difficult for traditional speech-to-text systems and/or digital assistants to identify as anything other than simple text. Accordingly, there is a need for digital assistants to be able to recognize when a user intends to input these special types of text via voice input, and process them appropriately.